Partially Synthesised Dataset to Improve Prediction Accuracy

Abstract

Abstract. The real world data sources, such as statistical agencies, library databanks and research institutes are the major data sources for researchers. Using this type of data involves several advantages including, the improvement of credibility and validity of the experiment and more importantly, it is related to a real world problems and typically unbiased. However, this type of data is most likely unavailable or inaccessible for everyone due to the following reasons. First, privacy and confidentiality concerns, since the data must to be protected on legal and ethical basis. Second, collecting real world data is costly and time consuming. Third, the data may be unavailable, particularly in the newly arises research subjects. Therefore, many studies have attributed the use of fully and/or partially synthesised data instead of real world data due to simplicity of creation, requires a relatively small amount of time and sufficient quantity can be generated to fit the requirements. In this context, this study introduces the use of partially synthesised data to improve the prediction of heart diseases from risk factors. We are proposing the generation of partially synthetic data from agreed principles using rule-based method, in which an extra risk factor will be added to the real-world data. In the conducted experiment, more than 85% of the data was derived from observed values (i.e., real-world data), while the remaining data has been synthetically generated using a rule-based method and in accordance with the World Health Organisation criteria. The analysis revealed an improvement of the variance in the data using the first two principal components of partially synthesised data. A further evaluation has been conducted using five popular supervised machine-learning classifiers. In which, partially synthesised data considerably improves the prediction of heart diseases. Where the majority of classifiers have approximately doubled their predictive performance using an extra risk factor.

Synthetic data in health care: A narrative review

“…Simulation and prediction research requires a large number of datasets to precisely predict behaviors and outcomes [31]. Real-world sources (e.g., from statistical agencies) have a significant advantage but are also most likely to be inaccessible to most researchers [32].…”

“…Synthetic data has been used in disease-specific hybrid simulation [33] and microsimulation for testing policy options [24,34] and health care financing strategies evaluation [35]. Studies also used synthetic data to validate simulation and prediction models [36] and to improve prediction accuracy [32].…”

Harnessing the power of synthetic data in healthcare: innovation, application, and privacy

“…Synthetic data has the potential to estimate the benefit of screening and healthcare policies, treatments, or clinical interventions, augment machine learning algorithms (e.g., image classification pipelines), pre-train machine learning models that can then be fine-tuned for specific patient populations, and improve public health models to predict outbreaks of infectious diseases [17][18][19][20][21][22][23][24][25][26][27] .…”

Toolbox for vessel X-ray angiography images simulation

“…An alternative to data augmentation is the creation of artificial images through the construction of images that mimic the appearance of vessel elements. Partially synthesised data proved to be useful in improving accuracy in machine learning classifiers [Aljaaf et al 2016]. The advantage of using artificial images such as those generated by Antczak et al [Antczak and Liberadzki 2018] for simulating vessels in angiographies is the fact that they are not related to the original dataset.…”